The goal of the sparse approximation problem is to approximate a target signal using a
linear combination of a few elementary signals drawn from a fixed collection. This paper …

In this review we focus on the technology associated with low-field NMR. We present the
current state-of-the-art in low-field NMR hardware and experiments, considering general …

This first chapter formulates the objectives of compressive sensing. It introduces the
standard compressive problem studied throughout the book and reveals its ubiquity in many …

This paper demonstrates theoretically and empirically that a greedy algorithm called
Orthogonal Matching Pursuit (OMP) can reliably recover a signal with m nonzero entries in …

The class of L1-regularized optimization problems has received much attention recently
because of the introduction of “compressed sensing,” which allows images and signals to be …

Compressive sampling offers a new paradigm for acquiring signals that are compressible
with respect to an orthonormal basis. The major algorithmic challenge in compressive …

Many problems in signal processing and statistical inference involve finding sparse
solutions to under-determined, or ill-conditioned, linear systems of equations. A standard …

In this paper we analyze several new methods for solving optimization problems with the
objective function formed as a sum of two terms: one is smooth and given by a black-box …

In this paper, we propose a robust visual tracking method by casting tracking as a sparse
approximation problem in a particle filter framework. In this framework, occlusion, noise, and …

A full-rank matrix \bfA∈R^n*m with n<m generates an underdetermined system of linear
equations \bfAx=\bfb having infinitely many solutions. Suppose we seek the sparsest …